code: def _split_right_context(
      self, utterance: torch.Tensor, right_context: torch.Tensor
  ) -> torch.Tensor:
      T, B, D = right_context.size()
      if T % self.right_context_length != 0:
          raise ValueError(
              "Tensor length should be divisible by its right context length"
          )
      num_segments = T // self.right_context_length
      # (num_segments, right context length, B, D)
      right_context_segments = right_context.reshape(
          num_segments, self.right_context_length, B, D
      )
      right_context_segments = right_context_segments.permute(
          0, 2, 1, 3
      ).reshape(num_segments * B, self.right_context_length, D)
  
      pad_segments = []  # [(kernel_size - 1, B, D), ...]
      for seg_idx in range(num_segments):
          end_idx = min(
              self.state_size + (seg_idx + 1) * self.segment_length,
              utterance.size(0),
          )
          start_idx = end_idx - self.state_size
          pad_segments.append(utterance[start_idx:end_idx, :, :])
  
      pad_segments = torch.cat(pad_segments, dim=1).permute(
          1, 0, 2
      )  # (num_segments * B, kernel_size - 1, D)
      return torch.cat(
          [pad_segments, right_context_segments], dim=1
      ).permute(0, 2, 1)
  
imports: import math
  from typing import List, Optional, Tuple
  import torch
  from torchaudio.models.emformer import _EmformerAttention, _EmformerImpl, _get_weight_init_gains
label_desc: Use image node_id 2 for calling the _ConvolutionModule obj's underlying member method code with example usage: obj._split_right_context(utterance, right_context) and returns: torch